As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

An energy storage device includes a sensor, a communication circuit and a processor. The sensor is configured to detect an abnormal event occurred in the energy storage device. The communication circuit is configured to connect to a local area network. The local area network includes a plurality of nodes formed by the energy storage device and other energy storage devices. The processor is configured to generate, according to the abnormal event detected by the sensor, historical usage data recording the abnormal event. In response to a trigger event, the processor is further configured to: update the historical usage data; and control the communication circuit to transmit at least part of the updated historical usage data to at least one energy storage device adjacent to the energy storage device in the local area network.

For improved provision of an alarm relating to anomaly scores assigned to input data, a method includes receiving input data relating to at least one device. The input data includes incoming data batches X relating to at least N separable classes. Respective anomaly scores are determined for the respective incoming data batch X relating to the at least N separable classes using N anomaly detection models. The anomaly detection models are applied to the input data to generate output data. A difference is determined, for the respective incoming data batch X, between the determined respective anomaly scores for the at least N separable classes and given respective anomaly scores of the N anomaly detection models. When the respective determined difference is greater than a difference threshold, an alarm relating to the determined difference is provided to a user, the respective device, and/or an IT system connected to the respective device.

The present disclosure provides a method for managing a pipe network of natural gas. The method may comprise: obtaining pipe network information of natural gas in at least one area, the pipe network information including a running time of a system of the pipe network of the natural gas and gas leakage information of thepipe network; extracting feature information based on the running time and the gas leakage information;predicting a maintenance time of the pipe network by inputting the feature information into a maintenance time prediction model.

The present disclosure provides a method for managing a pipe network of natural gas. The method may comprise: obtaining pipe network information of natural gas in at least one area, the pipe network information including a running time of a system of the pipe network of the natural gas and gas leakage information of the pipe network; extracting feature information based on the running time and the gas leakage information; predicting a maintenance time of the pipe network by inputting the feature information into a maintenance time prediction model.

The present invention relates to a method for maintaining a predictive value of a device through multiple control output signals, and there is an effect that operating information of a device in a normal state and operating information of a device shown before a malfunction occurs are collected, a distrust value is set based on the collected information, a collection value depending on operating information of the device collected in real time is compared with the distrust value, and a warning is given when a condition that an abnormal symptom of the device is doubted is satisfied to guide repairing and replacement of the device to be performed at an appropriate time, thereby preventing enormous loss of money due to the malfunction of the device in advance.

An example of a server including a communication interface to receive telemetry data from a plurality of client devices. The telemetry data is to indicate a health of a client device from the plurality of client devices. The server further includes a prediction engine to process the telemetry data to determine the health of the client device with a prediction model to identify a hardware issue at the client device. The server also includes a diagnostic evaluator in communication with the prediction engine. The diagnostic evaluator is to request a local confirmation of the hardware issue from the client device upon identification of the hardware issue by the prediction engine. The local confirmation is determined at the client device via a diagnostic engine. The server also includes a reporter to report the hardware issue upon receipt of the local confirmation.

An example of a server including a communication interface to receive telemetry data from a plurality of client devices. The telemetry data is to indicate a health of a client device from the plurality of client devices. The server further includes a prediction engine to process the telemetry data to determine the health of the client device with a prediction model to identify a hardware issue at the client device. The server also includes a diagnostic evaluator in communication with the prediction engine. The diagnostic evaluator is to request a local confirmation of the hardware issue from the client device upon identification of the hardware issue by the prediction engine. The local confirmation is determined at the client device via a diagnostic engine. The server also includes a reporter to report the hardware issue upon receipt of the local confirmation.

A system for reliable preventative maintenance of a device includes a telemetry component health predictor that generates predictive performance statistics for telemetry components performing telemetry collection or telemetry transmission operations for the device. The system further includes a predictive maintenance analytics engine that generates predictive performance statistics for the device based on device telemetry and the predictive performance statistics generated for the telemetry components of the device.

A remote water heater monitoring system is configured to communicate with a plurality of client water heaters over a network. The system processes received data related to the operation of the plurality of client water heaters and identifies one or more baseline trends over time related to water heater performance and/or water heater reliability using the received data from one or more of the client water heaters and identifies one or more abnormalities in the operation of a particular one of the client water heaters based on the baseline trends. An alert is generated for one or more of the abnormalities in the operation of the particular one of the client water heaters and is associated with a corresponding client account.